Drawing The Line From Automotive Data To Value-Added Services

Today, cars are equipped with a number of Advanced Driver Assistance Systems (ADAS) like adaptive cruise control, blind spot detector, lane departure warning system, automatic braking and lane centering, among many others. These systems have been developed and continue to be enhanced to assist the driver with the driving process, increasing vehicle safety and improving the overall user experience. The adoption of ADAS in modern vehicles has led to the generation of automotive data as it relies on inputs from multiple data sources, including sensors, computer vision, in-car networking and more. Connected cars generate about 25 gigabytes per hour and according to Gartner, there will be 250 millions of these connected cars by 2020. Imagine the amount of data each car will generate. What do we do with all this data? How can we derive value from it?

The Massive Data Sludge from Connected Cars

Unconnected cars meant no vehicle-generated data – at least none that made it to the cloud – and, without this data, automakers were more focused on the style and comfort of the vehicle. Today, there is access to massive amounts of automotive data, creating lucrative opportunities for automakers to not only reduce operational costs but also adopt new revenue generation models. Connected cars generate a few terabytes of data each day while semi-autonomous and autonomous vehicles generate even more data, close to 30 terabytes per day. Managing tons and tons of data is challenging. The capability to harness automotive data is becoming a substantial competitive advantage, beyond just insurance. Sure, all of this data can be transferred to the cloud, but however robust it may be, it cannot store and process that kind of data volume quickly. To overcome this problem, edge-based algorithms are being developed to enable the car’s computer to handle much of the data processing, enabling decisions to be made in real-time with low latency.

The biggest challenge is the capability to filter out critical data from the humongous amounts of automotive data generated by every sensor, every network and every system used in the vehicle. Automotive data can be broadly categorized into three categories – driver data, context data and vehicle data.

Driver data pertains to driver behavior, for example, what kind of music does the driver enjoy or during which times of the day does the driver usually drive?

Context data represents all of the data that is sourced from the surrounding environment, for example, what are the current weather conditions or how much traffic there is at the moment?

Vehicle data incorporates all the information about the vehicle, such as the performance of the vehicle, how much fuel or battery charge was used for the ride or if the vehicle is in need of repairs?

To ensure data privacy, the data can be categorized on the basis of the various stakeholders involved and access to the data can be restricted based on each role. This is extremely useful when data has to be shared with multiple parties.

Understanding the Customer through Artificial Intelligence

The next step is to make sense of the massive amounts of automotive data and this is where artificial intelligence comes in. With the rise of digitalization and the sharing economy, customer expectations have changed from just style and comfort of the vehicle to intelligent and convenient vehicles. Traditional cars are now becoming connected and will soon become fully autonomous with the help of artificial intelligence. It will enable vehicles to manage, make sense of and respond quickly to real-time data inputs from hundreds of different sensors. Tons of automotive data is being fed into machine learning algorithms to reveal valuable insights to automakers and the vehicle owners/users. Automakers are beginning to understand what benefits the customers are interested in while the vehicle owners/users can plan for maintenance schedules in advance with predictive analytics. Artificial intelligence is being used in ADAS-equipped vehicles to improve safety, energy efficiency and convenience for drivers. Deep learning is expected to be the most adopted approach to develop artificial intelligence as it learns and develops scenarios and algorithms to mimic real-world scenarios.

Collecting and analyzing massive volumes of data with agility and speed at scale is a requirement for making informed decisions in a situation where time correlation between data sets coming from two or more independent sources is critically important. Translating it all into a real-world challenge for artificially intelligent autonomous-driving systems, the expected outcome of such massive data processing is nothing short of getting the right answer in the shortest possible time to determine a proper action to avoid a traffic incident. Latency and signal update information is also of critical importance for two reasons. The first is to get to an almost real-time decision model. The second is to ensure that correct sets of data are combined and correlated in a way to derive the most optimal set of actions to ultimately determine the correct decision.

Creating Customer-Centric Value-Added Services

Now that we can make sense of the automotive data, we need to understand how to derive value-added services from it. According to McKinsey, the expected value from automotive data and shared mobility could reach $1.5 trillion by 2030. Automotive data is poised to generate value through increased revenues, reduced mobility cost and enhanced safety and security. ADAS-equipped vehicles are being developed with the objective of making them safer on the road. As more cars become software-defined, the need to protect them from cyber-attacks has also increased. As more cars become connected and autonomous, customers increasingly expect access to their favorite apps. Autonomous vehicles will free up about 50 minutes of commute time each day and will open the doors to entertainment, leisure and productivity for passengers. New business models such as trusted peer-to-peer platforms and usage-based insurance will gain more importance. After-sales cost reductions and revenue generation opportunities such as over-the-air (OTA) software updates will become important to keep the vehicle securely up-to-date throughout its entire lifecycle.

As industry verticals converge, mobility will become an integral part of most services. This will create additional value-added services, extending to third parties. Automakers and technology companies are still exploring such services that can be provided to customers and as they are starting to understand these needs and wants, the potential skyrockets. Automotive data has the ability to transform a vehicle into a moving retail store, a digital experience center and much more. Ultimately, regulations will play a decisive role in setting the boundaries of data ownership and transferability, allowing or limiting the proliferation of new use cases. However, customers will be the real winners of this evolution as they will benefit from a greater number of available features and services that will make transportation easier, safer, cheaper and much more convenient.

Companies in the automotive space should take an objective look at where they stand today with respect to user benefit understanding, organizational considerations and partnership challenges that lie ahead. After assessing their starting points, it will be important to quantify the value at stake and devote adequate management capacity and resources to speed up on the highway to data monetization.

As the auto industry is changed by technological and economic currents, OEMs and Tier-1 manufacturers will need to partner with technological specialists to thrive in the era of the software defined car. Movimento’s expertise is rooted in our background as an automotive company. This has allowed us to create the technological platform that underpins the future of the software driven and self-driven car. Connect with us today to learn more about how we can work together.